class_docstr: We'll use this mock dataset reader for most of the tests.
  
  It utilizes a transformers tokenizer, since historically we've seen deadlocking
  issues when using these within subprocesses. So these tests also serve as
  regression tests against those issues.
  
  And unlike `MockOldDatasetReader` below, it implements all of the new API,
  specifically the `apply_token_indexers` method, so that it can be used
  with num_workers > 0.
code: def text_to_instance(self, index: int, source: str, target: str = None) -> Instance:  # type: ignore
      fields: Dict[str, Field] = {}
      fields["source"] = TextField(self.tokenizer.tokenize(source))
      fields["index"] = MetadataField(index)  # type: ignore
      # It's important to have tests that use a tensor field since sending tensors
      # between processes has a lot of pitfalls.
      fields["tensor"] = TensorField(torch.tensor([1, 2, 3]))
      if target is not None:
          fields["target"] = TextField(self.tokenizer.tokenize(target))
      return Instance(fields)
  
imports: from typing import List, Iterable, Dict
  import torch
  import pytest
  from allennlp.common.testing import requires_gpu
  from allennlp.data.instance import Instance
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.data.data_loaders import MultiProcessDataLoader, WorkerError
  from allennlp.data.fields import Field, TextField, MetadataField, TensorField
  from allennlp.data.tokenizers import PretrainedTransformerTokenizer
  from allennlp.data.token_indexers import PretrainedTransformerIndexer
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.data.data_loaders.data_collator import LanguageModelingDataCollator
label_desc: Use image node_id 3 for calling the MockDatasetReader obj's underlying member method code with example usage: obj.text_to_instance(index, source, target) and returns: Instance